From 3e6c121aeb2db4419841387910a04041bb7f426e Mon Sep 17 00:00:00 2001
From: Philipp Niedermayer <p.niedermayer@gsi.de>
Date: Fri, 19 Aug 2022 14:51:55 +0200
Subject: [PATCH] Restructure module
---
plotting/__init__.py | 18 ++++
plotting/common.py | 60 ++++++++++++
plotting.py => plotting/other.py | 155 +------------------------------
plotting/spill.py | 140 ++++++++++++++++++++++++++++
4 files changed, 219 insertions(+), 154 deletions(-)
create mode 100644 plotting/__init__.py
create mode 100644 plotting/common.py
rename plotting.py => plotting/other.py (83%)
create mode 100644 plotting/spill.py
diff --git a/plotting/__init__.py b/plotting/__init__.py
new file mode 100644
index 0000000..b86897b
--- /dev/null
+++ b/plotting/__init__.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+""" Methos for plotting measurement data
+
+"""
+
+__author__ = "Philipp Niedermayer"
+__contact__ = "p.niedermayer@gsi.de"
+__date__ = "2022-05-04"
+
+
+from .spill import *
+from .other import *
+
+
+
+
diff --git a/plotting/common.py b/plotting/common.py
new file mode 100644
index 0000000..0f4212f
--- /dev/null
+++ b/plotting/common.py
@@ -0,0 +1,60 @@
+import scipy
+import scipy.signal
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import pint
+
+from ..data import *
+from ..fitting import *
+
+
+# Setup pint
+SI = pint.UnitRegistry()
+SI.setup_matplotlib()
+SI.default_format = '~P'
+
+
+# Colors
+# https://arxiv.org/abs/2107.02270
+petroff_colors = ["#3f90da", "#ffa90e", "#bd1f01", "#94a4a2", "#832db6", "#a96b59", "#e76300", "#b9ac70", "#717581", "#92dadd"]
+cmap_petroff_10 = mpl.colors.ListedColormap(petroff_colors, 'Petroff 10')
+mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=petroff_colors)
+
+cmap_petroff_gradient = mpl.colors.LinearSegmentedColormap.from_list('Petroff gradient', [petroff_colors[i] for i in (9,0,4,2,6,1)])
+cmap_petroff_gradient.set_under(petroff_colors[3])
+cmap_petroff_gradient.set_over(petroff_colors[7])
+mpl.rcParams['image.cmap'] = cmap_petroff_gradient
+
+cmap_petroff_bipolar = mpl.colors.LinearSegmentedColormap.from_list('Petroff bipolar', [petroff_colors[i] for i in (2,6,1,3,9,0,4)])
+cmap_petroff_bipolar.set_under(petroff_colors[5])
+cmap_petroff_bipolar.set_over(petroff_colors[8])
+
+
+# Matplotlib options
+mpl.rcParams.update({
+ 'figure.figsize': (8, 5), # Note: overwritten when using the %matplotlib magics
+ 'figure.dpi': 120, # Note: overwritten when using the %matplotlib magics
+ 'figure.constrained_layout.use': True,
+ 'legend.fontsize': 'x-small',
+ 'legend.title_fontsize': 'small',
+ 'grid.color': '#DDD',
+})
+
+
+
+
+# Utility methods
+
+
+
+def ax_set(ax, swap_xy=False, **kwargs):
+ if swap_xy:
+ nkwargs = {}
+ for k in kwargs:
+ nk = (('y' if k[0]=='x' else 'x') + k[1:]) if k[0] in 'xy' else k
+ nkwargs[nk] = kwargs[k]
+ ax.set(**nkwargs)
+ else:
+ ax.set(**kwargs)
+
diff --git a/plotting.py b/plotting/other.py
similarity index 83%
rename from plotting.py
rename to plotting/other.py
index 9890cb5..695cb3f 100644
--- a/plotting.py
+++ b/plotting/other.py
@@ -1,46 +1,7 @@
-import scipy
-import scipy.signal
-import numpy as np
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-import pint
-from .data import *
-from .fitting import *
+from .common import *
-# Setup pint
-SI = pint.UnitRegistry()
-SI.setup_matplotlib()
-SI.default_format = '~P'
-
-
-# Colors
-# https://arxiv.org/abs/2107.02270
-petroff_colors = ["#3f90da", "#ffa90e", "#bd1f01", "#94a4a2", "#832db6", "#a96b59", "#e76300", "#b9ac70", "#717581", "#92dadd"]
-cmap_petroff_10 = mpl.colors.ListedColormap(petroff_colors, 'Petroff 10')
-mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=petroff_colors)
-
-cmap_petroff_gradient = mpl.colors.LinearSegmentedColormap.from_list('Petroff gradient', [petroff_colors[i] for i in (9,0,4,2,6,1)])
-cmap_petroff_gradient.set_under(petroff_colors[3])
-cmap_petroff_gradient.set_over(petroff_colors[7])
-mpl.rcParams['image.cmap'] = cmap_petroff_gradient
-
-cmap_petroff_bipolar = mpl.colors.LinearSegmentedColormap.from_list('Petroff bipolar', [petroff_colors[i] for i in (2,6,1,3,9,0,4)])
-cmap_petroff_bipolar.set_under(petroff_colors[5])
-cmap_petroff_bipolar.set_over(petroff_colors[8])
-
-
-# Matplotlib options
-mpl.rcParams.update({
- 'figure.figsize': (8, 5), # Note: overwritten when using the %matplotlib magics
- 'figure.dpi': 120, # Note: overwritten when using the %matplotlib magics
- 'figure.constrained_layout.use': True,
- 'legend.fontsize': 'x-small',
- 'legend.title_fontsize': 'small',
- 'grid.color': '#DDD',
-})
-
def add_vline(axes, r, color='k', text=None, label=None, order=None, lw=1, text_top=True, text_vertical=True, zorder=-100, **kwargs):
@@ -240,15 +201,6 @@ def add_scale(ax, scale, text=None, *, vertical=False, size=0.01, padding=0.1, l
def v(swap_xy, x, y):
return (y, x) if swap_xy else (x, y)
-def ax_set(ax, swap_xy=False, **kwargs):
- if swap_xy:
- nkwargs = {}
- for k in kwargs:
- nk = (('y' if k[0]=='x' else 'x') + k[1:]) if k[0] in 'xy' else k
- nkwargs[nk] = kwargs[k]
- ax.set(**nkwargs)
- else:
- ax.set(**kwargs)
def smooth_plot(ax, x, y, smoothing=None, swap_xy=False, **kwargs):
@@ -760,108 +712,3 @@ def plot_beam_size(ax, ipm_data, xy, time_range=None, smoothing=None, **kwargs):
-# TDC data
-###########
-
-def plot_spill_intensity(ax, spill_data, bin_time=10e-6, *, rate=False, cumulative=False, relative=False, **plot_kwargs):
- """Plot spill intensity (binned particle counts) over extraction time
-
- :param ax: Axis to plot onto
- :param spill_data: Instance of TDCSpill
- :param bin_time: width of bins for particle counting in sec
- :param rate: if true, plot particle count rate, i.e. counts per s instead of per bin
- :param cumulative: if true, plot cumulative particle number
- :param relative: if true, plot fraction of total particle number
- """
- nbins = int(np.ceil(spill_data.length/bin_time))
-
- assert not (rate and cumulative), 'rate and cumulative plotting are mutually exclusive'
- weights = np.ones_like(spill_data.hittimes)
- if rate: weights /= bin_time
- if relative: weights /= spill_data.counts
- label = 'Extracted particle' + \
- (' fraction' if relative else 's') + \
- ('' if cumulative else \
- (' / s' if rate else f'\nper ${(bin_time*SI.s).to_compact():~gL}$ interval'))
-
- ax.hist(spill_data.hittimes, range=(spill_data.time_offset, spill_data.time_offset+bin_time*nbins), bins=nbins,
- histtype='step', cumulative=cumulative, weights=weights, **plot_kwargs)
- ax.set(ylabel=label, xlabel='Extraction time / s', #xlim=(spill_data.time_offset, spill_data.time_offset+bin_time*nbins),
- )
- if relative: ax.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(1))
-
-
-def plot_spill_duty_factor(ax, spill_data, counting_dt=10e-6, evaluation_dt=10e-3, *, show_poisson_limit=False, **kwargs):
- """Plot spill duty factor over extraction time
-
- :param ax: Axis to plot onto
- :param spill_data: Instance of TDCSpill
- :param counting_dt: width of bins for particle counting in sec
- :param evaluation_dt: width of bins for duty factor evaluation (value is rounded to the next multiple of count_bin_time)
-
- :param show_poisson_limit: if true, show poison limit of duty factor
-
- """
-
- counts, edges = spill_data.hitcounts(counting_dt, evaluation_dt)
-
- # spill duty factor
- F = np.mean(counts, axis=1)**2 / np.mean(counts**2, axis=1)
- s = ax.stairs(F, edges, **kwargs)
-
- if show_poisson_limit:
- Fp = np.mean(counts, axis=1) / ( np.mean(counts, axis=1) + 1 )
- ax.stairs(Fp, edges, label='Poisson limit', color=s.get_edgecolor() or 'gray', alpha=0.5,zorder=-1, lw=1, ls=':')
-
- ax.set(xlabel='Extraction time / s', ylim=(0,1.1),
- ylabel=f'Spill duty factor F\n(${(evaluation_dt*SI.s).to_compact():~gL}$ / ${(counting_dt*SI.s).to_compact():~gL}$ intervals)'
- )
-
-
-def plot_spill_consecutive_particle_delay(ax, spill_data, limit=5e-6, resolution=10e-9, show_poisson=False, **plot_kwargs):
- """Plot consecutive particle separation (particle-interval histograms)
-
- :param ax: Axis to plot onto
- :param spill_data: Instance of TDCSpill
- :param limit: maximum delay to plot (range of histogram)
- :param resolution: time resolution (bin width of histogram)
- """
- nbins = int(np.ceil(limit/resolution))
- delay = spill_data.hitdelay
- hist = _, t = np.histogram(delay, range=(0, resolution*nbins), bins=nbins) #, weights=np.ones_like(delay)/resolution)
- ax.stairs(*hist, **plot_kwargs)
- ax.set(xlabel='Delay between consecutive particles / s', xlim=(resolution, resolution*nbins),
- ylabel=f'particle count / ${(resolution*SI.s).to_compact():~gL}$ delay interval')
-
- if show_poisson:
- # plot poisson statistics (exponential distribution)
- l = spill_data.counts/spill_data.length
- p = np.exp(-l*t[:-1]) - np.exp(-l*t[1:]) # probability of delay in between t[i] and t[i+1]
- c = p*spill_data.counts
- c[c<1] = 0 # supress fractional counts
- ax.stairs(c, t, color='gray', label='Poisson statistics', zorder=-1, lw=1)
-
-
-def plot_spill_frequency_spectrum(ax, spill_data, fmax=100e3, **plot_kwargs):
- """Plot frequency spectrum of particle arrival times
-
- :param ax: Axis to plot onto
- :param spill_data: Instance of TDCSpill
- :param fmax: maximum frequency for analysis in Hz
- :param bins: number of bins for fft
- """
-
- # re-sample timestamps into equally sampled time series
- dt = 1/2/fmax
- timeseries, _ = spill_data.hitcounts(dt)
-
- # frequency analysis
- freq = np.fft.rfftfreq(len(timeseries), d=dt)[1:]
- mag = np.abs(np.fft.rfft(timeseries))[1:]
-
- # plot
- ax.plot(freq, mag, **plot_kwargs)
- ax.set(xlabel=f'Frequency / Hz', xlim=(0, fmax),
- ylabel='Magnitude / a.u.', ylim=(0, 1.1*max(mag)))
-
-
diff --git a/plotting/spill.py b/plotting/spill.py
new file mode 100644
index 0000000..178d960
--- /dev/null
+++ b/plotting/spill.py
@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+""" Methos for making spill realted plots
+
+"""
+
+__author__ = "Philipp Niedermayer"
+__contact__ = "p.niedermayer@gsi.de"
+__date__ = "2022-08-19"
+
+
+
+from .common import *
+
+
+
+
+def plot_spill_intensity(ax, spill_data, bin_time=10e-6, *, rate=False, cumulative=False, relative=False, **plot_kwargs):
+ """Plot spill intensity (binned particle counts) over extraction time
+
+ :param ax: Axis to plot onto
+ :param spill_data: Instance of spill data class, e.g. TDCSpill
+ :param bin_time: Width of bins for particle counting in sec
+ :param rate: If true, plot particle count rate, i.e. counts per s instead of per bin
+ :param cumulative: If true, plot cumulative particle number
+ :param relative: If true, plot fraction of total particle number
+ :param plot_kwargs: Keyword arguments to be passed to plotting method
+ """
+ assert not (rate and cumulative), 'rate and cumulative plotting are mutually exclusive'
+
+ # histogram in time bins
+ nbins = int(np.ceil(spill_data.length/bin_time))
+ weights = np.ones_like(spill_data.hittimes)
+ if rate: weights /= bin_time
+ if relative: weights /= spill_data.counts
+ hist, edges = np.histogram(spill_data.hittimes, bins=nbins, weights=weights,
+ range=(spill_data.time_offset, spill_data.time_offset+bin_time*nbins))
+ if cumulative: hist = np.cumsum(hist)
+
+ # plot
+ ax.stairs(hist, edges, **plot_kwargs)
+ if relative: ax.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(1))
+ ax.set(xlabel='Extraction time / s',
+ ylabel='Extracted particle' + \
+ (' fraction' if relative else 's') + \
+ ('' if cumulative else \
+ (' / s' if rate else f'\nper ${(bin_time*SI.s).to_compact():~gL}$ interval')),
+ )
+
+
+
+
+def plot_spill_duty_factor(ax, spill_data, counting_dt=10e-6, evaluation_dt=10e-3, *, show_poisson_limit=False, **plot_kwargs):
+ """Plot spill duty factor (<x>²/<x²>) over extraction time
+
+ :param ax: Axis to plot onto
+ :param spill_data: Instance of spill data class, e.g. TDCSpill
+ :param counting_dt: Width of bins for particle counting in sec
+ :param evaluation_dt: Width of bins for duty factor evaluation (value is rounded to the next multiple of count_bin_time)
+ :param show_poisson_limit: If true, show poison limit of duty factor
+ :param plot_kwargs: Keyword arguments to be passed to plotting method
+ """
+
+ counts, edges = spill_data.hitcounts(counting_dt, evaluation_dt)
+
+ # spill duty factor
+ F = np.mean(counts, axis=1)**2 / np.mean(counts**2, axis=1)
+ s = ax.stairs(F, edges, **plot_kwargs)
+
+ if show_poisson_limit:
+ Fp = np.mean(counts, axis=1) / ( np.mean(counts, axis=1) + 1 )
+ ax.stairs(Fp, edges, label='Poisson limit', color=s.get_edgecolor() or 'gray', alpha=0.5,zorder=-1, lw=1, ls=':')
+
+ ax.set(xlabel='Extraction time / s', ylim=(0,1.1),
+ ylabel=f'Spill duty factor F\n(${(evaluation_dt*SI.s).to_compact():~gL}$ / ${(counting_dt*SI.s).to_compact():~gL}$ intervals)'
+ )
+
+
+
+
+def plot_spill_consecutive_particle_delay(ax, spill_data, limit=5e-6, resolution=10e-9, show_poisson=False, swap_axis=False, log=True,
+ **plot_kwargs):
+ """Plot histogram of consecutive particle separation (particle-interval histograms)
+
+ :param ax: Axis to plot onto
+ :param spill_data: Instance of TDCSpill
+ :param limit: Maximum delay to plot (range of histogram)
+ :param resolution: Time resolution (bin width of histogram)
+ :param show_poisson: If true, show poison like distribution
+ :param log: If true, use logarithmic scaling
+ :param swap_axis: If True, swap plot axis
+ :param plot_kwargs: Keyword arguments to be passed to plotting method
+ """
+ nbins = int(np.ceil(limit/resolution))
+ delay = spill_data.hitdelay
+ hist = c, t = np.histogram(delay, range=(0, resolution*nbins), bins=nbins) #, weights=np.ones_like(delay)/resolution)
+ ax.stairs(*hist, orientation='horizontal' if swap_axis else 'vertical', **plot_kwargs)
+ ax_set(ax, swap_axis,
+ xlabel='Delay between consecutive particles / s', xlim=(resolution, resolution*nbins), xscale='log' if log else None,
+ ylabel=f'Particle count / ${(resolution*SI.s).to_compact():~gL}$ delay interval', yscale='log' if log else None)
+
+ if show_poisson:
+ # plot poisson statistics (exponential distribution)
+ l = spill_data.counts/spill_data.length
+ p = np.exp(-l*t[:-1]) - np.exp(-l*t[1:]) # probability of delay in between t[i] and t[i+1]
+ c = p*spill_data.counts
+ c[c<1] = 0 # supress fractional counts
+ ax.stairs(c, t, orientation='horizontal' if swap_axis else 'vertical', color='gray', label='Poisson statistics', zorder=-1, lw=1)
+
+
+
+
+def plot_spill_frequency_spectrum(ax, spill_data, fmax=100e3, **plot_kwargs):
+ """Plot frequency spectrum of particle arrival times
+
+ :param ax: Axis to plot onto
+ :param spill_data: Instance of TDCSpill
+ :param fmax: Maximum frequency for analysis in Hz
+ :param plot_kwargs: Keyword arguments to be passed to plotting method
+ """
+
+ # re-sample timestamps into equally sampled time series
+ dt = 1/2/fmax
+ timeseries, _ = spill_data.hitcounts(dt)
+
+ # frequency analysis
+ freq = np.fft.rfftfreq(len(timeseries), d=dt)[1:]
+ mag = np.abs(np.fft.rfft(timeseries))[1:]
+
+ # plot
+ ax.plot(freq, mag, **plot_kwargs)
+ ax.set(xlabel=f'Frequency / Hz', xlim=(0, fmax),
+ ylabel='Magnitude / a.u.', ylim=(0, 1.1*max(mag)))
+
+
+
+
+
+
\ No newline at end of file
--
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